Optimisation of the core subset for the APY approximation of genomic relationships.
Optimisation of the core subset for the APY approximation of genomic relationships.
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DOI：
10.1186/s12711-022-00767-x
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发表时间：
2022-11-22
影响因子：
4.1
通讯作者：
Gorjanc, Gregor
中科院分区：
文献类型：
--
作者：
Pocrnic, Ivan;Lindgren, Finn;Tolhurst, Daniel;Herring, William O.;Gorjanc, Gregor
随着基因组学进入超大规模时代，标准基因组评估模型由于其密集的数据结构和立方计算复杂性，面临着许多计算问题。已经提出了几种可扩展的方法来应对这一挑战，例如Proven and Young(APY)算法。在APY中，基因分型动物被划分为核心和非核心子集，这导致了基因组关系矩阵的稀疏逆。这种分区通常是随机进行的。虽然APY是完整模型的一个很好的近似值，但随机划分可能会使结果不稳定，可能会影响精度，甚至可能重新排序动物。在这里，我们通过选择具有最多信息的基因数据的动物来稳定地优化核心子集。我们提出了一种基于条件基因组关系矩阵或条件单核苷酸多态(SNP)基因矩阵的核心子集优化算法。我们比较了不同核心子集对模拟和真实猪数据集的基因组预测的准确性。核心子集由(1)随机、(2)基于基因组关系矩阵的对角线、(3)基于(2)的随机权重或(4)基于新的条件算法构建。为了了解不同的核心子集结构，我们使用线性主成分分析和非线性一致流形逼近和投影来可视化基因分型动物的种群结构。当核心动物的数量捕捉到基因组关系中的大部分变化时，所有核心子集的构建都表现得同样好，无论是在模拟数据集中还是在真实数据集中。当核心动物数量不够多时，随机结构的结果有很大的变异性，而条件结构的结果没有变异性。对种群结构和选定的核心动物的可视化显示，有条件的结构以可重复的方式将核心动物传播到整个基因分型动物领域。我们的结果证实了APY中核心子集的大小是关键的。此外，结果表明，核心子集可以通过条件算法进行优化，该算法实现了核心动物在基因分型动物领域中的最佳和可重复分布。网上版载有补充材料，可在10.1186/s12711-022-00767-x查阅。
By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed to address this challenge, such as the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of the genomic relationship matrix. This partitioning is often done at random. While APY is a good approximation of the full model, random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. We derived a novel algorithm for optimising the core subset based on a conditional genomic relationship matrix or a conditional single nucleotide polymorphism (SNP) genotype matrix. We compared the accuracy of genomic predictions with different core subsets for simulated and real pig data sets. The core subsets were constructed (1) at random, (2) based on the diagonal of the genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we visualise the population structure of the genotyped animals with linear Principal Component Analysis and non-linear Uniform Manifold Approximation and Projection. All core subset constructions performed equally well when the number of core animals captured most of the variation in the genomic relationships, both in simulated and real data sets. When the number of core animals was not sufficiently large, there was substantial variability in the results with the random construction but no variability with the conditional construction. Visualisation of the population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. Our results confirm that the size of the core subset in APY is critical. Furthermore, the results show that the core subset can be optimised with the conditional algorithm that achieves an optimal and repeatable spread of core animals across the domain of genotyped animals. The online version contains supplementary material available at 10.1186/s12711-022-00767-x.
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影响因子：
10.7
作者：
MacLeod IM;Larkin DM;Lewin HA;Hayes BJ;Goddard ME
通讯作者：
Goddard ME
影响因子：
3.3
作者：
Garcia, Andre L. S.;Masuda, Yutaka;Lourenco, Daniela
通讯作者：
Lourenco, Daniela
影响因子：
3.5
作者：
Masuda, Y.;Misztal, I.;Lawlor, T. J.
通讯作者：
Lawlor, T. J.
影响因子：
3.7
作者：
Datta A;Banerjee S;Finley AO;Gelfand AE
通讯作者：
Gelfand AE
影响因子：
4.1
作者：
Abdollahi-Arpanahi, Rostam;Lourenco, Daniela;Misztal, Ignacy
通讯作者：
Misztal, Ignacy